Senior Principal Applied Scientist

Oracle Life Sciences is focused on delivering software solutions to help the world’s largest pharmaceutical companies positively impact people’s lives by supporting the cost-effective development of treatments for today’s most challenging health related issues. 

We are looking for hands-on Applied Scientists with expertise and passion in solving difficult problems in life sciences. We intend to revolutionize the life sciences industry by leveraging AI/ML and Generative AI and redefine customer experience. 

This is a greenfield opportunity to design and build new AI native cloud applications from the ground up. We are growing fast, still at an early stage, and working on new initiatives. You will be part of a team of hard-working, motivated, a diverse set of people, and given the autonomy as well as support to do your best work. It is a dynamic and flexible workplace where you’ll belong and be encouraged. We operate with a startup mindset. We’re working on big goals, and we need talented folks with equally big ambitions. Join us!

About the Job

We're seeking a highly skilled Senior Principal Applied Scientist to modernize Oracle Life Sciences Safety One portfolio with AI native applications. As a Senior Principal Applied Scientist specializing in AI/ML, you will play a pivotal role in architecting, developing, and deploying state-of-the-art models to power our SafetyOne product suite. You will collaborate closely with cross-functional teams including product managers, software engineers, and data scientists to unlock machine learning capabilities in all our products. Our new platform will be built directly on Oracle Cloud Infrastructure (OCI) based on cloud native principles. We build to scale globally, leveraging state-of-the-art tooling, with zero downtime.

Responsibilities

• Work with Engineering team for training data acquisition and data analysis
• Define and follow the process for ML use case evaluation and planning
• Design, develop, and optimize RAG (Retrieval-Augmented Generation) models to facilitate effective information retrieval and integration of structured data, ontologies, knowledge graphs, or other forms of structured knowledge representation 
• Utilize vector databases and advanced indexing techniques to efficiently store and retrieve relevant information for conversational contexts
• Explore, Fine-tune and optimize large language models such as Cohere for specific use cases in the Life sciences.
• Evaluate model performance, interpret results, experiment with various training strategies and domain-specific fine-tuning to improve accuracy and efficiency.
• Implement and experiment with cutting-edge NLP, NLU, and NLG techniques to solve various use cases.
• Collaborate with engineers to integrate machine learning models into production systems, ensuring scalability, reliability, and performance
• Interacts with product and service teams to identify questions and issues for data analysis and experiments. 
• Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources. 
• Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers.

Qualification

• Master's degree or PhD in Computer Science, Engineering, Mathematics, or related experience 
• 10+ years of experience in data science
• Strong programming skills in Python, SQL and proficiency with machine learning libraries such as TensorFlow, PyTorch, or R.
• Experience with cloud platforms (e.g., AWS, OCI) and containerization technologies (e.g., Docker, Kubernetes).
• Solid understanding of NLP fundamentals and experience with NLU/NLG techniques such as sentiment analysis, entity recognition, and text generation.
• Preference for expertise in developing RAG models, working with vector databases, and fine-tuning large language models.
• Experience in life sciences and healthcare domain and experience in a complex global organization is a plus
• Excellent problem-solving abilities and a pragmatic approach to building scalable and robust machine learning systems.
• Strong communication skills with the ability to collaborate effectively with cross-functional teams and articulate complex tec